Hanyar tura KV cache tana da mahimmanci lokacin da aka maimaita farkon tambayoyi a cikin zirga-zirgar LLM \u0257inku. Idan bu\u0199atar da ta dace ta sauka a kan kwafin da ya dace, injin sabis zai iya sake amfani da yanayin hankali da aka adana maimakon sake lissafin wa\u0257annan alamomin prefill sau da sau.<\/p>\n\n\n\n
Wannan yana kama da cikakken bayani na kayan aiki, amma yana zama matsalar samfur da sauri. Dogayen tambayoyin tsarin, mahallin RAG, misalan few-shot, da tarihin tattaunawa mai juyawa na iya sa aikin prefill ya yi tsada. Lokacin da kowace kwafi ta sake lissafin farkon tambaya \u0257aya, \u0199ungiyoyi suna biyan lokaci, lokacin GPU, da tsare-tsaren \u0199arfin aiki.<\/p>\n\n\n\n
ShareAI yana ba masu ha\u0253aka API \u0257aya don samfura 150+, bayyanuwar kasuwa, tura tambayoyi, da sauya matsala. Hanyar tura KV cache tana zaune a mataki \u0257aya \u0199asa, a cikin kayan aikin sabis na samfur. Abin da ya dace ga masu karatun ShareAI yana da sau\u0199i: yanke shawarar tura tambayoyi yana da mahimmanci a kowane mataki na tsarin AI, daga za\u0253in samfur har zuwa wane kwafin GPU ke sarrafa tambaya mai maimaitawa.<\/p>\n\n\n\n
Lokacin fassarar LLM, samfurin yana fara sarrafa tambayar shigarwa a matakin prefill. Yana gina ajiyar ma\u0253alli-da-daraja, wanda yawanci ake kira KV cache, don haka alamomin da aka samar daga baya za su iya komawa ga mahallin da aka riga aka sarrafa.<\/p>\n\n\n\n
Ajiyar farkon tambaya yana ba injinan sabis damar sake amfani da wannan ajiyar lokacin da tambaya ta gaba ta raba farkon tambayar iri \u0257aya. Takaddun ajiyar farkon tambaya ta atomatik na vLLM<\/a> yana bayyana wannan a matsayin sake amfani da KV cache don farkon tambayoyi da aka raba don tambayar sabuwa ta iya tsallake lissafi don \u0253angaren da aka raba. Ajiyar farkon tambaya ta SGLang<\/a> yana amfani da ra'ayi mai ala\u0199a don raba KV cache don jerin alamomin gama gari.<\/p>\n\n\n\n Wannan yana da mahimmanci musamman ga nau'ikan aiki inda tambayoyi da yawa ke farawa iri \u0257aya: wakilan tallafi tare da dogon tambayar tsarin, aikace-aikacen RAG da ke amfani da maimaita takardun, wakilan lamba tare da umarnin ma'ajiyar bayanai, ko samfuran tattaunawa da ke \u0257aukar tarihin tattaunawa a cikin juyawa.<\/p>\n\n\n\n Ajiyar farkon tambaya ya fi sau\u0199i a kan kwafi \u0257aya. Tsarin iri \u0257aya yana ganin farkon tambaya mai maimaitawa kuma zai iya sake amfani da ajiyarsa idan akwai \u0199wa\u0199walwar ajiya. Matsalar tana bayyana lokacin da sabis \u0257in ya fa\u0257a\u0257a a kwance.<\/p>\n\n\n\n Tare da mai daidaita nauyi na round-robin na yau da kullum, tambaya ta farko na iya kunna ajiyar a kan kwafi A, yayin da tambaya ta biyu tare da farkon tambaya iri \u0257aya ta sauka a kan kwafi B. Kwafi B ba shi da wannan yanayin da aka adana, don haka yana sake lissafin aikin prefill iri \u0257aya. Tambaya ta uku na iya zuwa kwafi C kuma ta rasa kuma.<\/p>\n\n\n\n Yayin da adadin kwafi ya \u0199aru, daidaita nauyi mai sau\u0199i na iya rarraba tambayoyi masu ala\u0199a a kan \u0199arin injuna. Rundunar sabis na samfur na iya zama daidaitacce, amma adadin samun ajiyar farkon tambaya yana raguwa. Wannan shi ne gibin da hanyar tura KV cache ke \u0199o\u0199arin cikewa.<\/p>\n\n\n\n Dacewar zama yana jagorantar zirga-zirgar daga mai amfani \u0257aya, wurin aiki, mai haya, ko tattaunawa zuwa kwafi \u0257aya. Ita ce mafi sau\u0199i wajen farawa don tattaunawa mai juyawa saboda tambayoyin da ke bi sau da yawa suna raba mahallin da ya gabata.<\/p>\n\n\n\n Matsalar ita ce cewa ainihin mai amfani ba koyaushe yake daidai da kamanceceniya tambaya ba. Mutane biyu na iya raba dogon tsarin tambaya \u0257aya kuma har yanzu a jagoranci su zuwa kwafi daban-daban. Dacewar zama kuma na iya rikicewa lokacin da aka \u0199ara ko cire kwafi.<\/p>\n\n\n\n Hanyar prefix-hash tana amfani da tambayar kanta a matsayin ma\u0253allin hanyar. Mai jagoranci yana yin hash na farkon tambayar mai tsayayye kuma yana aika daidai prefixes zuwa kwafi \u0257aya.<\/p>\n\n\n\n Wannan yana aiki mafi kyau lokacin da tambayoyin tsarin da ake maimaitawa, misalan few-shot, ko mahallin da aka samo wanda aka raba ya fi mahimmanci fiye da ainihin mai amfani. Mafi wahala shi ne za\u0253ar iyakar prefix. Idan hash ya ha\u0257a da alamar lokaci, ID na bu\u0199ata, ko filin na musamman na mai amfani, ma\u0253allin hanyar yana rarrabuwa kuma sake amfani da cache yana rushewa.<\/p>\n\n\n\n Mafi ci gaba yana bin wa\u0257anne tubalan cache suke kan wane kwafi, sannan yana jagorantar kowace bu\u0199ata zuwa kwafi tare da mafi kyawun daidaiton cache yayin da har yanzu yana la'akari da nauyi. aikin na'ura mai ba da hanya tsakanin hanyoyin sadarwa llm-d<\/a> yana bayyana mai za\u0253ar \u0199arshen da ke la'akari da KV-cache locality, nauyin yanzu, da fifiko lokacin za\u0253ar inda bu\u0199ata ya kamata ta je.<\/p>\n\n\n\n Wannan ya fi rikitarwa, amma yana daidai ga manyan rukunin aiki inda rashin cache ake aunawa, mai tsada, kuma mai yawan faruwa.<\/p>\n\n\n\n Hanyar KV cache ba ta da daraja ta atomatik saboda rikitarwa. Ba ta dace sosai ba lokacin da tambayoyin suka kasance gajeru, mafi yawan na musamman, ko aka sarrafa su a cikin batches tare da \u0199aramin tsari mai maimaitawa.<\/p>\n\n\n\n Takaitaccen takardu, \u0199ir\u0199irar kirkira, cirewa na lokaci \u0257aya, da yawancin ayyukan batch na asinkron ba za su iya samun isasshen daidaiton prefix da aka raba don tabbatar da hanyar cache-aware ba. A irin wa\u0257annan lokuta, daidaiton nauyi na yau da kullum na iya zama mafi tsabta.<\/p>\n\n\n\n Gwajin aikace-aikace yana auna: adadin samun cache, lokaci zuwa farkon alamar, yawan aiki, zurfin jerin aiki, matsin \u0199wa\u0199walwar GPU, da farashin kowace aikin da aka kammala. Idan hanyar cache-aware ba ta motsa wa\u0257annan lambobin ba, gyara tsarin tambaya da farko.<\/p>\n\n\n\n ShareAI kasuwa ce ta AI da API, ba mai daidaita nauyin samfurin AI a cikin rukunin GPU \u0257inku ba. Masu ha\u0253akawa suna amfani da ShareAI don samun dama ga samfura da yawa ta hanyar API \u0257aya, kwatanta siginar kasuwa, tsara bu\u0199atu, sarrafa amfani, da kuma sauyawa lokacin da wata hanya ta lalace.<\/p>\n\n\n\n Wannan har yanzu yana sa hanyar KV cache ta zama mai mahimmanci. Idan kuna sarrafa tsarin fassarar ku na kanku, yana taimaka muku yin tambayoyi mafi kyau game da kayan aiki. Idan kuna amfani da samfuran da aka shirya, yana taimaka muku tantance dalilin da yasa hanyoyi biyu masu suna iri \u0257aya na samfurin na iya yin aiki daban-daban a \u0199ar\u0199ashin nauyin aiki na gaske.<\/p>\n\n\n\n Ga Masu Gina, wannan kuma yana da ala\u0199a da farashi. Wani app mai dogon tambayoyi, maimaita mahallin RAG, ko madaukai na wakili na iya haifar da amfani da AI mara daidaito sosai. ShareAI Builder yana ba masu mallakar aikace-aikace damar tsara zirga-zirgar fassarar AI ta hanyar ShareAI, saita riba ko \u0199arin farashi, bari abokan ciniki su biya ShareAI don amfani da aka tsara, kuma su kar\u0253i biyan ku\u0257i na wata-wata bisa ga amfani da aka samar. Aikace-aikacen kansa yana ci gaba da kasancewa a waje da ShareAI.<\/p>\n\n\n\n Don za\u0253in samfur da kimanta hanya, fara da kasuwar samfuran ShareAI<\/a>. Don abubuwan aiwatarwa na asali, yi amfani da Manuniya API na ShareAI<\/a>.<\/p>\n\n\n\n Hanyar sadarwar KV cache wata dabara ce ta sadarwa da ke aika bu\u0199atu tare da maimaita farkon tambayoyi zuwa kwafi wa\u0257anda ake tsammanin suna ri\u0199e da daidaitaccen KV cache. Manufar ita ce rage maimaita lissafin prefill.<\/p>\n\n\n\n Adana farkon tambayoyi ita ce ikon injin samfurin amfani da yanayin da aka adana don farkon tambayoyi da aka raba. Hanyar sadarwar KV cache ita ce dabarar sanya zirga-zirga da ke taimakawa bu\u0199atun da suka dace su isa inda wannan yanayin da aka adana yake.<\/p>\n\n\n\n Hanyar sadarwar round-robin tana rarraba bu\u0199atu a tsakanin kwafi ba tare da sanin wane kwafi ke da wane farkon tambaya da aka adana ba. Wata tambaya da aka maimaita na iya rasa cache saboda kawai ta sauka a wani kwafi daban.<\/p>\n\n\n\n Tattaunawa mai juyawa da yawa, RAG, wakilan lamba, wakilan tallafi, tambayoyi masu \u0257an ka\u0257an, da aikace-aikace tare da dogayen tsarin tambayoyi da aka raba su ne mafi dacewa saboda suna sake amfani da manyan farkon tambayoyi.<\/p>\n\n\n\n Kauce mata idan tambayoyin suna da gajarta, mafi yawansu na musamman ne, ko kuma suna da tsarin tsari tare da \u0199aramin maimaituwa. A irin wa\u0257annan lokuta, rikitarwa na hanyar sadarwa na iya \u0199ara \u0199aramin amfani.<\/p>\n\n\n\n Eh. vLLM yana daftarin adana farkon tambayoyi ta atomatik, kuma SGLang yana daftarin adana farkon tambayoyi don raba KV cache a cikin jerin alamu na gama gari. Har yanzu injin samfurin yana bu\u0199atar taimakon hanyar sadarwa idan kwafi da yawa suna cikin aiki.<\/p>\n\n\n\n A'a. Hanyar sadarwar KV cache tana aiki tare da daidaitaccen ko kusan daidaitaccen amfani da farkon tambayoyi a cikin hidimar fahimta. Adana ma'ana tana adana da sake amfani da amsoshi ko sakamakon tsaka-tsaki bisa ma'ana, yawanci tare da ha\u0257e-ha\u0257e ko iyakokin kamance.<\/p>\n\n\n\n A'a. ShareAI kasuwa ce ta AI da kuma API layer don samun damar samfurori, hanya, failover, amfani, da kuma biyan ku\u0257i. KV-cache-aware routing shine \u0199ananan matakin kayan aikin samfurin samfurin don \u0199ungiyoyin da ke aiki da kwafin inference.<\/p>\n\n\n\n Masu Gina ya kamata su \u0257auki halayen cache a matsayin \u0257aya daga cikin abubuwan da ke haifar da farashi a cikin aikace-aikacen da ke da nauyin AI. Idan aikace-aikacen su yana da amfani mara daidaito, ShareAI na iya taimakawa wajen tsara da samun ku\u0257i daga wannan zirga-zirgar AI yayin da aikace-aikacen ya kasance an gina shi kuma an mallake shi a wajen ShareAI.<\/p>\n\n\n\n Auna \u0199imar cache hit, lokaci zuwa farkon token, throughput, zurfin layi, matsin lamba na VRAM, farashi a kowace aiki, da ingancin fitarwa. Canje-canje na hanya ya kamata su inganta nauyin aiki, ba kawai dashboard ba.<\/p>\n\n\n\n Zai iya rage farashin kayan aiki ga \u0199ungiyoyin da ke hidimar samfurori da kansu saboda \u0199arancin aikin prefill mai maimaitawa na iya inganta ingancin GPU. Ga APIs da aka kar\u0253a, tasirin ya dogara da ko mai bayarwa ya bayyana wa\u0257annan tanadin a cikin farashi ko aiki.<\/p>","protected":false},"excerpt":{"rendered":" Hanyar tura KV cache tana aika maimaitattun gabanin tambaya zuwa kwafi wa\u0257anda za su iya sake amfani da yanayin hankali da aka adana, yana taimaka wa \u0199ungiyoyi rage aikin sake cika LLM da ba dole ba.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"cta-title":"Explore AI Models","cta-description":"Compare price, latency, and availability across providers.","cta-button-text":"Browse Models","cta-button-link":"https:\/\/shareai.now\/models\/?utm_source=blog&utm_medium=content&utm_campaign=kv-cache-routing-llm-prefill","rank_math_title":"KV Cache Routing: Cut Redundant LLM Prefill Work","rank_math_description":"KV cache routing sends repeated prompt prefixes to the right replica so LLM teams can reduce redundant prefill work and latency.","rank_math_focus_keyword":"KV cache routing, prefix-aware routing, prefix caching, LLM inference optimization","footnotes":""},"categories":[4,6],"tags":[176,173,175,174,178,177],"class_list":["post-3047","post","type-post","status-publish","format-standard","hentry","category-developers","category-insights","tag-ai-routing","tag-kv-cache-routing","tag-llm-inference","tag-prefix-caching","tag-sglang","tag-vllm"],"_links":{"self":[{"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/posts\/3047","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/comments?post=3047"}],"version-history":[{"count":1,"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/posts\/3047\/revisions"}],"predecessor-version":[{"id":3089,"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/posts\/3047\/revisions\/3089"}],"wp:attachment":[{"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/media?parent=3047"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/categories?post=3047"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/tags?post=3047"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Inda Round-Robin Ya Kasa<\/h2>\n\n\n\n
Matakai Uku na Hanyar Gudanarwa Mai Aiki<\/h2>\n\n\n\n
1. Dacewar Zama<\/h3>\n\n\n\n
2. Hanyar Prefix-Hash<\/h3>\n\n\n\n
3. Hanyar Cache-Event-Aware<\/h3>\n\n\n\n
Lokacin Da Za A Tsallake Shi<\/h2>\n\n\n\n
Yadda Wannan Ya Dace Da ShareAI<\/h2>\n\n\n\n
Jerin Duba Hanyar KV Cache<\/h2>\n\n\n\n
Tambayoyi akai-akai (FAQ).<\/h2>\n\n\n\n
Menene hanyar sadarwar KV cache?<\/h3>\n\n\n
Ta yaya hanyar sadarwar KV cache ta bambanta da adana farkon tambayoyi?<\/h3>\n\n\n
Me yasa hanyar sadarwar round-robin ke cutar da adana farkon tambayoyi?<\/h3>\n\n\n
Wa\u0257anne nau'ikan aiki ne suka fi amfana daga hanyar sadarwar KV cache?<\/h3>\n\n\n
Yaushe ya kamata wata \u0199ungiya ta kauce wa hanyar sadarwar KV cache?<\/h3>\n\n\n
Shin vLLM da SGLang suna tallafawa adana farkon tambayoyi?<\/h3>\n\n\n
Shin hanyar sadarwar KV cache iri \u0257aya ce da adana ma'ana?<\/h3>\n\n\n
Shin ShareAI yana maye gurbin mai daidaita nauyin da ke da masaniyar KV cache?<\/h3>\n\n\n
Ta yaya ya kamata Masu Gina su yi tunani game da hanyar KV cache?<\/h3>\n\n\n
Me ya kamata \u0199ungiyoyi su auna kafin su canza hanyar?<\/h3>\n\n\n
Shin hanyar KV cache na iya rage farashin API na AI?<\/h3>\n\n\n